{
 "cells": [
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   "source": [
    "<p align=\"center\">\n",
    "    <img src=\"https://github.com/GeostatsGuy/GeostatsPy/blob/master/TCG_color_logo.png?raw=true\" width=\"220\" height=\"240\" />\n",
    "\n",
    "</p>\n",
    "\n",
    "## Subsurface Data Analytics \n",
    "\n",
    "## Artificial Neural Networks for Prediction in Python\n",
    "\n",
    "#### Honggeun Jo, Graduate Student, The University of Texas at Austin\n",
    "\n",
    "##### [LinkedIn](https://www.linkedin.com/in/honggeun-jo/?originalSubdomain=kr) | [Twitter](https://twitter.com/HonggeunJ)\n",
    "\n",
    "#### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1) | [GeostatsPy](https://github.com/GeostatsGuy/GeostatsPy)\n",
    "\n",
    "### PGE 383 Exercise: Support Vector Machine for Subsurface Modeling in Python \n",
    "\n",
    "Here's a simple workflow, demonstration of neural networks for subsurface modeling workflows. This should help you get started with building subsurface models that use data analytics and machine learning. Here's some basic details about neural networks.  \n",
    "\n",
    "#### Neural Networks\n",
    "\n",
    "Machine learning method for supervised learning for classification and regression analysis.  Here are some key aspects of support vector machines.\n",
    "\n",
    "**Basic Design** *\"...a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs.\"* Caudill (1989). \n",
    "\n",
    "**Nature-inspire Computing** based on the neuronal structure in the brain, including many interconnected simple, processing units, known as nodes that are capable of complicated emergent pattern detection due to a large number of nodes and interconnectivity.\n",
    "\n",
    "**Training and Testing** just like and other predictive model (e.g. linear regression, decision trees and support vector machines) we perform training to fit parameters and testing to tune hyper parameters.\n",
    "\n",
    "**Parameters** are the weights applied to each connection and a bias term applied to each node.  For a single node artificial neural network, we could compare the weights to the $\\beta$ slope term and the bias to the $\\beta_{0}$ intercept term.\n",
    "\n",
    "\\begin{equation}\n",
    "Y = \\beta X + \\beta_0\n",
    "\\end{equation}\n",
    "\n",
    "the number of parameters increases rapidly as we increase the number of nodes and the connectivity between the nodes.\n",
    "\n",
    "**Layers** the typical artificial neural net is structured with an **input layer**, with one node for each $m$ predictor feature, $X_1,\\ldots,X_m$. There is an **ouput layer**, with one node for each $r$ response feature, $Y_1,\\ldots,Y_r$.  There may be one or more layers of nodes between the input and output layers, known as **hidden layer(s)**.  \n",
    "\n",
    "**Connections** are the linkages between the nodes in adjacent layers.  For example, in a fully connected artificial neural network, all the input nodes are connected to all of the nodes in the first layer, then all of the nodes in the first layer are connected to the next layer and so forth. Each connection includes a weight parameter as indicated above.\n",
    "\n",
    "**Nodes** receive the weighted signal from the connected previous layer nodes, sum and then apply this result to the **activation** function in the node. Some example activation functions include:\n",
    "\n",
    "* **Binary** the node fires or not.  This is represented by a Heaviside step function.\n",
    "\n",
    "* **Identify** the input is passed to the output $f(x) = x$\n",
    "\n",
    "* **Linear** the node passes a signal that increases linearly with the weighted input.\n",
    "\n",
    "* **Logistic** also known as sigmoid or soft step $f(x) = \\frac{1}{1+e^{-x}}$\n",
    "\n",
    "the result of the linearly weighted inputs applied to the nonlinear activation is output from the node to the next layer of connected nodes.\n",
    "\n",
    "**Training Cycles / Epochs** include the presentation of new data, forward application of the current prediction model to make estimates, calculation of error and then backpropagation of error to correct the network weights to reduce the error.\n",
    "\n",
    "**Batch** is the set of training data drawn to train for each epoch. There is a trade-off, a larger batch results in more computational time per epoch, but a more accurate estimate of the error to adjust the weights.  Smaller batches result in a nosier estimate of the error, but faster epochs, this results in faster learning and even possibly more robust models.\n",
    "\n",
    "**Local Minimums** if one calculated the error hypersurface for a range of model parameters it would be hyparabolic, there is a global minimium error solution.  But this error hyper surface is rough and it is possible to be stuck in a local minimum. **Learning Rate** and **Mommentum** coefficients are introduced to avoid getting stuck in local minimums.\n",
    "\n",
    "* **Mommentum** is a hyperparameter to control the use of information from the weight update over the last epoch for consideration in the current epoch.  This can be accomplished with an update vector, $v_i$, a mommentum parameter, $\\alpha$, to calculate the current weight update, $v_{i+1}$ given the new update $\\theta_{i+1}$.\n",
    "\n",
    "\\begin{equation}\n",
    "v_{i+1} = \\alpha v_i + \\theta_{i+1}\n",
    "\\end{equation}\n",
    "\n",
    "* **Learning Rate** is a hyperparameter that controls the adjustment of the weights in response to the gradient indicated by backpropagation of error \n",
    "\n",
    "##### Applications to subsurface modeling\n",
    "\n",
    "We demonstrate the estimation of porosity between wells from seismic accoustic impedance. \n",
    "\n",
    "* modeling a nonlinear relationship between acoustic impedance and porosity \n",
    "\n",
    "#### Limitations of Neural Network Estimation\n",
    "\n",
    "Since we demonstrate the use of a neural network to estimate porosity inter-well from seismic, we should comment on limitations of neural networks for estimation:\n",
    "\n",
    "* does not honor the well data\n",
    "\n",
    "* does not honor the histogram of the data\n",
    "\n",
    "* does not honor spaital correlation \n",
    "\n",
    "* does not honor the multivariate relationship\n",
    "\n",
    "* generally low interpretability models\n",
    "\n",
    "* requires a large number of data for effective training\n",
    "\n",
    "* high model complexity with high model variance\n",
    "\n",
    "\n",
    "#### Workflow Goals\n",
    "\n",
    "Learn the basics of machine learning in python to predict subsurface features. This includes:\n",
    "\n",
    "* Loading and visualizing sample data\n",
    "* Trying out neural nets\n",
    "\n",
    "#### Objective \n",
    "\n",
    "In the PGE 383: Subsurface Machine Learning Class, I want to provide hands-on experience with building subsurface modeling workflows. Python provides an excellent vehicle to accomplish this. I have coded a package called GeostatsPy with GSLIB: Geostatistical Library (Deutsch and Journel, 1998) functionality that provides basic building blocks for building subsurface modeling workflows. \n",
    "\n",
    "The objective is to remove the hurdles of subsurface modeling workflow construction by providing building blocks and sufficient examples. This is not a coding class per se, but we need the ability to 'script' workflows working with numerical methods.    \n",
    "\n",
    "#### Getting Started\n",
    "\n",
    "Here's the steps to get setup in Python with the GeostatsPy package:\n",
    "\n",
    "1. Install Anaconda 3 on your machine (https://www.anaconda.com/download/). \n",
    "2. From Anaconda Navigator (within Anaconda3 group), go to the environment tab, click on base (root) green arrow and open a terminal. \n",
    "3. In the terminal type: pip install geostatspy. \n",
    "4. Open Jupyter and in the top block get started by copy and pasting the code block below from this Jupyter Notebook to start using the geostatspy functionality. \n",
    "\n",
    "You will need to copy the data file to your working directory.  They are available here:\n",
    "\n",
    "* Tabular data - 12_sample_data.csv found [here](https://github.com/GeostatsGuy/GeoDataSets/blob/master/12_sample_data.csv).\n",
    "\n",
    "There are exampled below with these functions. You can go here to see a list of the available functions, https://git.io/fh4eX, other example workflows and source code. \n",
    "\n",
    "#### Import Required Packages\n",
    "\n",
    "We will also need some standard packages. These should have been installed with Anaconda 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geostatspy.GSLIB as GSLIB          # GSLIB utilies, visualization and wrapper\n",
    "import geostatspy.geostats as geostats    # GSLIB methods convert to Python        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also need some standard packages. These should have been installed with Anaconda 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np                        # ndarrys for gridded data\n",
    "import pandas as pd                       # DataFrames for tabular data\n",
    "import os                                 # set working directory, run executables\n",
    "import matplotlib.pyplot as plt           # for plotting\n",
    "import seaborn as sns                     # for plotting\n",
    "import warnings                           # supress warnings from seaborn pairplot\n",
    "from sklearn.model_selection import train_test_split # train / test DatFrame split\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also need the following packages to train and test our artificial neural nets:\n",
    "\n",
    "* Tensorflow - open source machine learning \n",
    "\n",
    "* Keras - high level application programing interface (API) to build and train models\n",
    "\n",
    "More information is available at [tensorflow install](https://www.tensorflow.org/install).\n",
    "\n",
    "* This workflow was designed with tensorflow version 1.13.1 and does not work with version 2.0.0 alpha.\n",
    "\n",
    "To check your current version of tensorflow you could run the next block of code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\pm27995\\AppData\\Local\\anaconda3\\Lib\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'2.15.0'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "tf.__version__                            # check the installed version of tensorflow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's import all of the tensorflow and keras methods that we will need in our workflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from keras.models import Sequential, load_model\n",
    "from keras.layers import Dense, Dropout, Activation\n",
    "#from keras.layers.core import Dense, Dropout, Activation\n",
    "#from keras.utils import np_utils\n",
    "from keras import utils\n",
    "import keras\n",
    "from tensorflow.python.keras import backend as k"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set the working directory\n",
    "\n",
    "I always like to do this so I don't lose files and to simplify subsequent read and writes (avoid including the full address each time). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(\"C:/PGE383\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading Data\n",
    "Let's load the provided multivariate, spatial dataset '12_sample_data.csv'.  It is a comma delimited file with: \n",
    "\n",
    "* X and Y coordinates ($m$)\n",
    "* facies 0 and 1 \n",
    "* porosity (fraction)\n",
    "* permeability ($mD$)\n",
    "* acoustic impedance ($\\frac{kg}{m^3} \\cdot \\frac{m}{s} \\cdot 10^3$). \n",
    "\n",
    "We load it with the pandas 'read_csv' function into a data frame we called 'df' and then preview it to make sure it loaded correctly.\n",
    "\n",
    "**Python Tip: using functions from a package** just type the label for the package that we declared at the beginning:\n",
    "\n",
    "```python\n",
    "import pandas as pd\n",
    "```\n",
    "\n",
    "so we can access the pandas function 'read_csv' with the command: \n",
    "\n",
    "```python\n",
    "pd.read_csv()\n",
    "```\n",
    "\n",
    "but read csv has required input parameters. The essential one is the name of the file. For our circumstance all the other default parameters are fine. If you want to see all the possible parameters for this function, just go to the docs [here](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html).  \n",
    "\n",
    "* The docs are always helpful\n",
    "* There is often a lot of flexibility for Python functions, possible through using various inputs parameters\n",
    "\n",
    "also, the program has an output, a pandas DataFrame loaded from the data.  So we have to specficy the name / variable representing that new object.\n",
    "\n",
    "```python\n",
    "df = pd.read_csv(\"12_sample_data.csv\")  \n",
    "```\n",
    "\n",
    "Let's run this command to load the data and then look at the resulting DataFrame to ensure that we loaded it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r'https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/12_sample_data.csv')\n",
    "df = df.sample(frac=.30, random_state = 73073); df = df.reset_index()\n",
    "df['logPerm'] = np.log10(df['Perm'].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's add a facies label for shale and sand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Facies_Label']=np.zeros(len(df));\n",
    "df['Facies_Label']= np.where(df['Facies']==1, 'Sand', 'Shale')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get a quick preview of our new DataFrame, 'df' with the built in function.\n",
    "\n",
    "```python\n",
    "df.head()\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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       "      <th>index</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>Perm</th>\n",
       "      <th>AI</th>\n",
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       "      <td>3222.716042</td>\n",
       "      <td>2696.102930</td>\n",
       "      <td>3.508222</td>\n",
       "      <td>Sand</td>\n",
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       "      <td>3.042590</td>\n",
       "      <td>5500.997419</td>\n",
       "      <td>0.483243</td>\n",
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       "      <td>400.298484</td>\n",
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       "      <td>2.602384</td>\n",
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       "      <td>699.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.159171</td>\n",
       "      <td>9.224292</td>\n",
       "      <td>5263.064063</td>\n",
       "      <td>0.964933</td>\n",
       "      <td>Shale</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>333</td>\n",
       "      <td>571</td>\n",
       "      <td>340.0</td>\n",
       "      <td>549.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.170881</td>\n",
       "      <td>84.471492</td>\n",
       "      <td>2918.232227</td>\n",
       "      <td>1.926710</td>\n",
       "      <td>Shale</td>\n",
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       "      <td>39.837129</td>\n",
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       "      <td>1.600288</td>\n",
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       "      <td>2.796235</td>\n",
       "      <td>Sand</td>\n",
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       "      <td>Sand</td>\n",
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       "      <td>540.0</td>\n",
       "      <td>859.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.173662</td>\n",
       "      <td>39.910675</td>\n",
       "      <td>3414.962471</td>\n",
       "      <td>1.601089</td>\n",
       "      <td>Shale</td>\n",
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       "      <td>779.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>11.171392</td>\n",
       "      <td>3646.918994</td>\n",
       "      <td>1.048107</td>\n",
       "      <td>Shale</td>\n",
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      "text/plain": [
       "    index  Unnamed: 0      X      Y  Facies  Porosity         Perm  \\\n",
       "0      72          94  250.0   50.0     0.0  0.139637     0.347182   \n",
       "1     153         232  650.0  750.0     0.0  0.170732    10.720560   \n",
       "2     258         439   80.0  669.0     1.0  0.244345  3222.716042   \n",
       "3      56          73  200.0  150.0     0.0  0.167125     3.042590   \n",
       "4     303         521   60.0  929.0     1.0  0.216253   400.298484   \n",
       "5     374         645  750.0  699.0     0.0  0.159171     9.224292   \n",
       "6     333         571  340.0  549.0     0.0  0.170881    84.471492   \n",
       "7       1           2   50.0  850.0     1.0  0.237154    39.837129   \n",
       "8      54          71  200.0  250.0     1.0  0.188043    57.804784   \n",
       "9     311         535  250.0  579.0     1.0  0.215039   625.510541   \n",
       "10      8          10   50.0  450.0     1.0  0.200389   265.636019   \n",
       "11    269         458  540.0  859.0     0.0  0.173662    39.910675   \n",
       "12    279         485  580.0  779.0     0.0  0.168371    11.171392   \n",
       "\n",
       "             AI   logPerm Facies_Label  \n",
       "0   4747.274043 -0.459442        Shale  \n",
       "1   4535.625583  1.030217        Shale  \n",
       "2   2696.102930  3.508222         Sand  \n",
       "3   5500.997419  0.483243        Shale  \n",
       "4   3959.934912  2.602384         Sand  \n",
       "5   5263.064063  0.964933        Shale  \n",
       "6   2918.232227  1.926710        Shale  \n",
       "7   3074.562617  1.600288         Sand  \n",
       "8   4997.078597  1.761964         Sand  \n",
       "9   2693.691341  2.796235         Sand  \n",
       "10  3454.389302  2.424287         Sand  \n",
       "11  3414.962471  1.601089        Shale  \n",
       "12  3646.918994  1.048107        Shale  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(n=13)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary Statistics for Tabular Data\n",
    "\n",
    "There are a lot of efficient methods to calculate summary statistics from tabular data in DataFrames. The describe command provides count, mean, minimum, maximum, and quartiles all in a nice data table. We use transpose just to flip the table so that features are on the rows and the statistics are on the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <td>144.0</td>\n",
       "      <td>261.048611</td>\n",
       "      <td>136.830267</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>144.750000</td>\n",
       "      <td>270.000000</td>\n",
       "      <td>381.250000</td>\n",
       "      <td>478.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <td>144.0</td>\n",
       "      <td>440.291667</td>\n",
       "      <td>245.046303</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>217.250000</td>\n",
       "      <td>459.000000</td>\n",
       "      <td>659.250000</td>\n",
       "      <td>825.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X</th>\n",
       "      <td>144.0</td>\n",
       "      <td>449.375000</td>\n",
       "      <td>263.691435</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>242.500000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>650.000000</td>\n",
       "      <td>980.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>144.0</td>\n",
       "      <td>542.979167</td>\n",
       "      <td>289.228936</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>579.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>979.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Facies</th>\n",
       "      <td>144.0</td>\n",
       "      <td>0.659722</td>\n",
       "      <td>0.475456</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Porosity</th>\n",
       "      <td>144.0</td>\n",
       "      <td>0.190700</td>\n",
       "      <td>0.031972</td>\n",
       "      <td>0.131230</td>\n",
       "      <td>0.166621</td>\n",
       "      <td>0.188733</td>\n",
       "      <td>0.217234</td>\n",
       "      <td>0.256172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perm</th>\n",
       "      <td>144.0</td>\n",
       "      <td>510.036736</td>\n",
       "      <td>1136.459068</td>\n",
       "      <td>0.039555</td>\n",
       "      <td>6.950509</td>\n",
       "      <td>56.886770</td>\n",
       "      <td>356.658709</td>\n",
       "      <td>7452.343369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AI</th>\n",
       "      <td>144.0</td>\n",
       "      <td>3746.825725</td>\n",
       "      <td>793.196589</td>\n",
       "      <td>1961.600397</td>\n",
       "      <td>3167.631744</td>\n",
       "      <td>3668.526774</td>\n",
       "      <td>4244.264532</td>\n",
       "      <td>6194.573653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logPerm</th>\n",
       "      <td>144.0</td>\n",
       "      <td>1.646059</td>\n",
       "      <td>1.210694</td>\n",
       "      <td>-1.402796</td>\n",
       "      <td>0.841941</td>\n",
       "      <td>1.754955</td>\n",
       "      <td>2.552252</td>\n",
       "      <td>3.872293</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count         mean          std          min          25%  \\\n",
       "index       144.0   261.048611   136.830267     1.000000   144.750000   \n",
       "Unnamed: 0  144.0   440.291667   245.046303     2.000000   217.250000   \n",
       "X           144.0   449.375000   263.691435     0.000000   242.500000   \n",
       "Y           144.0   542.979167   289.228936    19.000000   300.000000   \n",
       "Facies      144.0     0.659722     0.475456     0.000000     0.000000   \n",
       "Porosity    144.0     0.190700     0.031972     0.131230     0.166621   \n",
       "Perm        144.0   510.036736  1136.459068     0.039555     6.950509   \n",
       "AI          144.0  3746.825725   793.196589  1961.600397  3167.631744   \n",
       "logPerm     144.0     1.646059     1.210694    -1.402796     0.841941   \n",
       "\n",
       "                    50%          75%          max  \n",
       "index        270.000000   381.250000   478.000000  \n",
       "Unnamed: 0   459.000000   659.250000   825.000000  \n",
       "X            400.000000   650.000000   980.000000  \n",
       "Y            579.000000   800.000000   979.000000  \n",
       "Facies         1.000000     1.000000     1.000000  \n",
       "Porosity       0.188733     0.217234     0.256172  \n",
       "Perm          56.886770   356.658709  7452.343369  \n",
       "AI          3668.526774  4244.264532  6194.573653  \n",
       "logPerm        1.754955     2.552252     3.872293  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this table we set some reasonable feature ranges for plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "pormin = 0.13; pormax = 0.26              # porosity min and max\n",
    "permmin = 0.01; permmax = 3000            # permeability min and max\n",
    "AImin = 1000.0; AImax = 8000              # acoustic impedance min and max\n",
    "Fmin = 0; Fmax = 1                        # facies min and max\n",
    "cmap = plt.cm.plasma                      # color map for plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Seismically-Derived Map\n",
    "\n",
    "Let's load a seismically-derived map of acoustic impedance.\n",
    "\n",
    "* This seismic acoustic impedance matches the well data acoustic impedance at the well locations\n",
    "\n",
    "* This seismic data has better coverage than the well data, but with larger scale and error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "seismic = np.loadtxt(open(\"12_AI.csv\", \"rb\"), delimiter=\",\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's set the map parameters:\n",
    "\n",
    "* X min and max\n",
    "\n",
    "* Y min and max\n",
    "\n",
    "* number of cells in the X and Y directions\n",
    "\n",
    "* isotropic cell size\n",
    "\n",
    "and then plot the loaded seismic map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xmin = 0.0; xmax = 1000.0                 # range of x values\n",
    "ymin = 0.0; ymax = 1000.0                 # range of y values\n",
    "ny = 100; nx = 100                        # number of cell in Y and X\n",
    "cell_size = 10.0;                         # grid cell size\n",
    "\n",
    "plt.subplot(111)\n",
    "GSLIB.pixelplt_st(seismic,xmin,xmax,ymin,ymax,cell_size,AImin,AImax,'Acoustic Impedance','X(m)','Y(m)','Acoustic Impedance (kg/m2s*10^3)',cmap)\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=1.2, top=1.2, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Extract Variables of Interest\n",
    "\n",
    "Let's extract the facies, porosity, log permeability and acoustic impedance.\n",
    "\n",
    "* the extracted DataFrame subset is a shallow copy, any changes in DataFrame 'df_subset' update the same entries in 'df'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_subset = df.loc[:,['Facies','Porosity','logPerm','AI']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Always good to preview the DataFrame everytime we work with it to make sure it is correct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>logPerm</th>\n",
       "      <th>AI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.139637</td>\n",
       "      <td>-0.459442</td>\n",
       "      <td>4747.274043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.170732</td>\n",
       "      <td>1.030217</td>\n",
       "      <td>4535.625583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.244345</td>\n",
       "      <td>3.508222</td>\n",
       "      <td>2696.102930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.167125</td>\n",
       "      <td>0.483243</td>\n",
       "      <td>5500.997419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.216253</td>\n",
       "      <td>2.602384</td>\n",
       "      <td>3959.934912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.159171</td>\n",
       "      <td>0.964933</td>\n",
       "      <td>5263.064063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.170881</td>\n",
       "      <td>1.926710</td>\n",
       "      <td>2918.232227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.237154</td>\n",
       "      <td>1.600288</td>\n",
       "      <td>3074.562617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.188043</td>\n",
       "      <td>1.761964</td>\n",
       "      <td>4997.078597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.215039</td>\n",
       "      <td>2.796235</td>\n",
       "      <td>2693.691341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.200389</td>\n",
       "      <td>2.424287</td>\n",
       "      <td>3454.389302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.173662</td>\n",
       "      <td>1.601089</td>\n",
       "      <td>3414.962471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.168371</td>\n",
       "      <td>1.048107</td>\n",
       "      <td>3646.918994</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Facies  Porosity   logPerm           AI\n",
       "0      0.0  0.139637 -0.459442  4747.274043\n",
       "1      0.0  0.170732  1.030217  4535.625583\n",
       "2      1.0  0.244345  3.508222  2696.102930\n",
       "3      0.0  0.167125  0.483243  5500.997419\n",
       "4      1.0  0.216253  2.602384  3959.934912\n",
       "5      0.0  0.159171  0.964933  5263.064063\n",
       "6      0.0  0.170881  1.926710  2918.232227\n",
       "7      1.0  0.237154  1.600288  3074.562617\n",
       "8      1.0  0.188043  1.761964  4997.078597\n",
       "9      1.0  0.215039  2.796235  2693.691341\n",
       "10     1.0  0.200389  2.424287  3454.389302\n",
       "11     0.0  0.173662  1.601089  3414.962471\n",
       "12     0.0  0.168371  1.048107  3646.918994"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_subset.head(n=13)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Multivariate Relationships\n",
    "\n",
    "Before we build a predictive model, it is a good idea to attempt to understand the multivariate relationships between the variables. Let's calculate a matrix scatter plots to understand how the data parameters are correlated to each other, 2 variables at a time: \n",
    "\n",
    "* linearility vs. non-linearity\n",
    "\n",
    "* homo- vs. heteroscedasticity, \n",
    "\n",
    "* constraints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 826.25x750 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "warnings.filterwarnings('ignore')         # supress warning from the seaborn.pairplot         \n",
    "sns.pairplot(df_subset, hue = 'Facies')\n",
    "sns.set(style=\"ticks\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Normalization\n",
    "\n",
    "We must normalize the features before we apply them in a neural network model.\n",
    "\n",
    "* We will set the features to range from -1 to 1.\n",
    "\n",
    "These are the motivations for this normalization:\n",
    "\n",
    "* remove effect of scale of different type of data (i.e., acoustic impedance varies between 2000~6000 but porosity only varies between 0~0.3)\n",
    "\n",
    "* activation functions in Neural networks have more sensivity when the values of nodes are closer to 0.0 (i.e., results in higher gradient and improves backpropagation in training)\n",
    "\n",
    "Let's normalize each feature.  \n",
    "\n",
    "* It is easy to backtransform given we keep track of the original min and max values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "por_min = df_subset['Porosity'].min(); por_max = df_subset['Porosity'].max();\n",
    "logperm_min = df_subset['logPerm'].min(); logperm_max = df_subset['logPerm'].max();\n",
    "AI_min = df_subset['AI'].min(); AI_max = df_subset['AI'].max();\n",
    "\n",
    "df_subset['norm_Porosity'] = (df_subset['Porosity'] - por_min)/(por_max - por_min) * 2 - 1\n",
    "df_subset['norm_logPerm'] = (df_subset['logPerm'] - logperm_min)/(logperm_max - logperm_min) * 2 - 1\n",
    "df_subset['norm_AI'] = (df_subset['AI'] - AI_min)/(AI_max - AI_min) * 2 - 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the update to our DataFrame with a quick preview."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>logPerm</th>\n",
       "      <th>AI</th>\n",
       "      <th>norm_Porosity</th>\n",
       "      <th>norm_logPerm</th>\n",
       "      <th>norm_AI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.139637</td>\n",
       "      <td>-0.459442</td>\n",
       "      <td>4747.274043</td>\n",
       "      <td>-0.865422</td>\n",
       "      <td>-0.642337</td>\n",
       "      <td>0.316178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.170732</td>\n",
       "      <td>1.030217</td>\n",
       "      <td>4535.625583</td>\n",
       "      <td>-0.367672</td>\n",
       "      <td>-0.077546</td>\n",
       "      <td>0.216178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.244345</td>\n",
       "      <td>3.508222</td>\n",
       "      <td>2696.102930</td>\n",
       "      <td>0.810690</td>\n",
       "      <td>0.861966</td>\n",
       "      <td>-0.652961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.167125</td>\n",
       "      <td>0.483243</td>\n",
       "      <td>5500.997419</td>\n",
       "      <td>-0.425413</td>\n",
       "      <td>-0.284926</td>\n",
       "      <td>0.672298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.216253</td>\n",
       "      <td>2.602384</td>\n",
       "      <td>3959.934912</td>\n",
       "      <td>0.361002</td>\n",
       "      <td>0.518526</td>\n",
       "      <td>-0.055825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.159171</td>\n",
       "      <td>0.964933</td>\n",
       "      <td>5263.064063</td>\n",
       "      <td>-0.552734</td>\n",
       "      <td>-0.102298</td>\n",
       "      <td>0.559879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.170881</td>\n",
       "      <td>1.926710</td>\n",
       "      <td>2918.232227</td>\n",
       "      <td>-0.365287</td>\n",
       "      <td>0.262351</td>\n",
       "      <td>-0.548010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.237154</td>\n",
       "      <td>1.600288</td>\n",
       "      <td>3074.562617</td>\n",
       "      <td>0.695581</td>\n",
       "      <td>0.138591</td>\n",
       "      <td>-0.474146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.188043</td>\n",
       "      <td>1.761964</td>\n",
       "      <td>4997.078597</td>\n",
       "      <td>-0.090559</td>\n",
       "      <td>0.199889</td>\n",
       "      <td>0.434206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.215039</td>\n",
       "      <td>2.796235</td>\n",
       "      <td>2693.691341</td>\n",
       "      <td>0.341574</td>\n",
       "      <td>0.592023</td>\n",
       "      <td>-0.654101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.200389</td>\n",
       "      <td>2.424287</td>\n",
       "      <td>3454.389302</td>\n",
       "      <td>0.107058</td>\n",
       "      <td>0.451002</td>\n",
       "      <td>-0.294685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.173662</td>\n",
       "      <td>1.601089</td>\n",
       "      <td>3414.962471</td>\n",
       "      <td>-0.320773</td>\n",
       "      <td>0.138895</td>\n",
       "      <td>-0.313314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.168371</td>\n",
       "      <td>1.048107</td>\n",
       "      <td>3646.918994</td>\n",
       "      <td>-0.405462</td>\n",
       "      <td>-0.070763</td>\n",
       "      <td>-0.203719</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Facies  Porosity   logPerm           AI  norm_Porosity  norm_logPerm  \\\n",
       "0      0.0  0.139637 -0.459442  4747.274043      -0.865422     -0.642337   \n",
       "1      0.0  0.170732  1.030217  4535.625583      -0.367672     -0.077546   \n",
       "2      1.0  0.244345  3.508222  2696.102930       0.810690      0.861966   \n",
       "3      0.0  0.167125  0.483243  5500.997419      -0.425413     -0.284926   \n",
       "4      1.0  0.216253  2.602384  3959.934912       0.361002      0.518526   \n",
       "5      0.0  0.159171  0.964933  5263.064063      -0.552734     -0.102298   \n",
       "6      0.0  0.170881  1.926710  2918.232227      -0.365287      0.262351   \n",
       "7      1.0  0.237154  1.600288  3074.562617       0.695581      0.138591   \n",
       "8      1.0  0.188043  1.761964  4997.078597      -0.090559      0.199889   \n",
       "9      1.0  0.215039  2.796235  2693.691341       0.341574      0.592023   \n",
       "10     1.0  0.200389  2.424287  3454.389302       0.107058      0.451002   \n",
       "11     0.0  0.173662  1.601089  3414.962471      -0.320773      0.138895   \n",
       "12     0.0  0.168371  1.048107  3646.918994      -0.405462     -0.070763   \n",
       "\n",
       "     norm_AI  \n",
       "0   0.316178  \n",
       "1   0.216178  \n",
       "2  -0.652961  \n",
       "3   0.672298  \n",
       "4  -0.055825  \n",
       "5   0.559879  \n",
       "6  -0.548010  \n",
       "7  -0.474146  \n",
       "8   0.434206  \n",
       "9  -0.654101  \n",
       "10 -0.294685  \n",
       "11 -0.313314  \n",
       "12 -0.203719  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_subset.head(n=13)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also a good idea to check the summary statistics.  \n",
    "\n",
    "* All continuous features should now range from -1.0 to 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Facies</th>\n",
       "      <td>144.0</td>\n",
       "      <td>0.659722</td>\n",
       "      <td>0.475456</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Porosity</th>\n",
       "      <td>144.0</td>\n",
       "      <td>0.190700</td>\n",
       "      <td>0.031972</td>\n",
       "      <td>0.131230</td>\n",
       "      <td>0.166621</td>\n",
       "      <td>0.188733</td>\n",
       "      <td>0.217234</td>\n",
       "      <td>0.256172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logPerm</th>\n",
       "      <td>144.0</td>\n",
       "      <td>1.646059</td>\n",
       "      <td>1.210694</td>\n",
       "      <td>-1.402796</td>\n",
       "      <td>0.841941</td>\n",
       "      <td>1.754955</td>\n",
       "      <td>2.552252</td>\n",
       "      <td>3.872293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AI</th>\n",
       "      <td>144.0</td>\n",
       "      <td>3746.825725</td>\n",
       "      <td>793.196589</td>\n",
       "      <td>1961.600397</td>\n",
       "      <td>3167.631744</td>\n",
       "      <td>3668.526774</td>\n",
       "      <td>4244.264532</td>\n",
       "      <td>6194.573653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>norm_Porosity</th>\n",
       "      <td>144.0</td>\n",
       "      <td>-0.048027</td>\n",
       "      <td>0.511789</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.433483</td>\n",
       "      <td>-0.079520</td>\n",
       "      <td>0.376700</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>norm_logPerm</th>\n",
       "      <td>144.0</td>\n",
       "      <td>0.155945</td>\n",
       "      <td>0.459023</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.148929</td>\n",
       "      <td>0.197231</td>\n",
       "      <td>0.499519</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>norm_AI</th>\n",
       "      <td>144.0</td>\n",
       "      <td>-0.156515</td>\n",
       "      <td>0.374770</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.430173</td>\n",
       "      <td>-0.193509</td>\n",
       "      <td>0.078516</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count         mean         std          min          25%  \\\n",
       "Facies         144.0     0.659722    0.475456     0.000000     0.000000   \n",
       "Porosity       144.0     0.190700    0.031972     0.131230     0.166621   \n",
       "logPerm        144.0     1.646059    1.210694    -1.402796     0.841941   \n",
       "AI             144.0  3746.825725  793.196589  1961.600397  3167.631744   \n",
       "norm_Porosity  144.0    -0.048027    0.511789    -1.000000    -0.433483   \n",
       "norm_logPerm   144.0     0.155945    0.459023    -1.000000    -0.148929   \n",
       "norm_AI        144.0    -0.156515    0.374770    -1.000000    -0.430173   \n",
       "\n",
       "                       50%          75%          max  \n",
       "Facies            1.000000     1.000000     1.000000  \n",
       "Porosity          0.188733     0.217234     0.256172  \n",
       "logPerm           1.754955     2.552252     3.872293  \n",
       "AI             3668.526774  4244.264532  6194.573653  \n",
       "norm_Porosity    -0.079520     0.376700     1.000000  \n",
       "norm_logPerm      0.197231     0.499519     1.000000  \n",
       "norm_AI          -0.193509     0.078516     1.000000  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_subset.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Separation of Training and Testing Data\n",
    "\n",
    "We also need to split our data into training / testing datasets so that we:\n",
    "\n",
    "* can train our neural networks using training data \n",
    "\n",
    "* while testing their performance with validating data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_subset.iloc[:,[6]]                 # extract the predictor feature - acoustic impedance\n",
    "y = df_subset.iloc[:,[4]]                 # extract the response feature - porosity\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=73073)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize are train and test data split on a single scatter plot.\n",
    "\n",
    "* we want to make sure it is fair\n",
    "\n",
    "* ensure that the test samples are not clustered or too far away from the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(121)\n",
    "plt.plot(X_train['norm_AI'].values,y_train['norm_Porosity'].values, 'o', markerfacecolor='red', markeredgecolor='black', alpha=0.4, label = \"Train\")\n",
    "plt.plot(X_test['norm_AI'].values,y_test['norm_Porosity'].values, 'o', markerfacecolor='blue', markeredgecolor='black', alpha=0.4, label = \"Test\")\n",
    "plt.legend(loc='upper right');\n",
    "plt.title('Train and Test: Porosity vs. Acoustic Impedance')\n",
    "plt.ylabel('Porosity (Normalized)')\n",
    "plt.xlabel('Acoustic Impedance (Normalized)')\n",
    "plt.xlim(-1,1); plt.ylim(-1,1)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.2, top=1.2, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Build and Train a Simple Neural Network\n",
    "\n",
    "For our first model let's try something very simple.  \n",
    "\n",
    "* 1 predictor feature - acoustic impedance\n",
    "\n",
    "* 1 responce feature - porosity\n",
    "\n",
    "We will build a model to predict porosity from acoustic impedance over all locations in our model $\\bf{u} \\in AOI$. \n",
    "\n",
    "\\begin{equation}\n",
    "\\phi(\\bf{u}) = \\hat{f}(AI(\\bf{u})\n",
    "\\end{equation}\n",
    "\n",
    "We will use this model to inform porosity between the wells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build Neural Network for prediction model (input: AI - > Output: Porosity)\n",
    "model_1 = Sequential([\n",
    "    Dense(1, activation='relu', input_shape=(1,)),\n",
    "    Dense(10, activation='relu'),\n",
    "    Dense(1, activation='tanh'),\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have specified a simple neural net with:\n",
    "\n",
    "* 1 input node \n",
    "\n",
    "* 3 nodes in the hidden layer\n",
    "\n",
    "* 1 output node\n",
    "\n",
    "Now we specify the optimization method and the related hyperparameters (e.g.,learning rate, etc)\n",
    "further information of Keras optimizers are available [here](https://keras.io/optimizers)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "keras.optimizers.Adam(learning_rate=0.003,beta_1=0.9,beta_2=0.999,epsilon=1e-7,weight_decay=0.0,amsgrad=False)\n",
    "\n",
    "model_1.compile(optimizer='adam',loss='mse',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are ready to supply our normalized predictor feature to predict the normalized responce feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "hist_1 = model_1.fit(X_train, y_train,batch_size=5, epochs=300,validation_data=(X_test, y_test),verbose = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the training curve, loss function for our model.\n",
    "\n",
    "* this is a measure of the inaccuracy\n",
    "\n",
    "* **square loss** ($L_2$ loss) is the:\n",
    "\n",
    "\\begin{equation}\n",
    "L_2 = \\sum_{\\bf{u}_{\\alpha} \\in AOI} \\left(y(\\bf{u}_{\\alpha}) - \\hat{f}(x_1(\\bf{u}_{\\alpha}),\\ldots,x_m(\\bf{u}_{\\alpha})\\right)\n",
    "\\end{equation}\n",
    "\n",
    "We can see the progress of the model over epochs in reduction of training and testing error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2,1,1)\n",
    "plt.plot(hist_1.history['loss'])\n",
    "plt.plot(hist_1.history['val_loss'])\n",
    "plt.title('Loss function of Model 1')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper right')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize the model in prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 55ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "AIbins = np.linspace(df_subset['AI'].values.min(), df_subset['AI'].values.max(), 30) # set the bins for prediction\n",
    "norm_AIbins = (AIbins-AI_min)/(AI_max-AI_min)*2-1 # use normalized bins\n",
    "\n",
    "pred_norm_porsity = model_1.predict(np.array(norm_AIbins)) # predict with our ANN\n",
    "\n",
    "pred_porosity = (pred_norm_porsity + 1)/2*(por_max - por_min)+por_min # transform it back to original range\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(df_subset['AI'].values,df_subset['Porosity'].values,'ko', alpha = 0.4)\n",
    "plt.plot(AIbins,pred_porosity,'r-',linewidth=5)\n",
    "plt.xlabel('Acoustic Impedance (kg/m2s*10^6)')\n",
    "plt.ylabel('Porosity (faction)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Neural Net-Based Map of Porosity from Seismic Acoustic Impedance\n",
    "\n",
    "Now let's predict porosity at all locations from the seismic data. First we normalize the seismic data.  \n",
    "\n",
    "* We may have some values below -1.0 and above 1.0 as we are using the transform applied to the acoustic impedance at the wells.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 0s 978us/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "norm_seismic = (seismic-AI_min)/(AI_max-AI_min)*2-1\n",
    "\n",
    "pred_norm_porosity_map = model_1.predict(np.array(norm_seismic.flatten())) # predict with our ANN\n",
    "\n",
    "pred_porosity_map = (pred_norm_porosity_map + 1)/2*(por_max - por_min)+por_min # transform it back to original range\n",
    "pred_porosity_map = np.reshape(pred_porosity_map,(ny,nx))\n",
    "\n",
    "plt.subplot(121)\n",
    "GSLIB.pixelplt_st(norm_seismic,xmin,xmax,ymin,ymax,cell_size,-1,1,'Acoustic Impedance','X(m)','Y(m)','Acoustic Impedance (normalized)',cmap)\n",
    "\n",
    "plt.subplot(122)\n",
    "GSLIB.pixelplt_st(pred_porosity_map,xmin,xmax,ymin,ymax,cell_size,por_min,por_max,'Predicted Porosity','X(m)','Y(m)','Porosity (fraction)',cmap)\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.2, top=1.2, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizing the Neural Net\n",
    "\n",
    "There are some methods available to interogate the artificial neural net.\n",
    "\n",
    "* neural net summary\n",
    "\n",
    "* weights\n",
    "\n",
    "Here's the summary from our neural net.  It lists by layers the number of nodes and number of parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_3 (Dense)             (None, 1)                 2         \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 10)                20        \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 1)                 11        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 33 (132.00 Byte)\n",
      "Trainable params: 33 (132.00 Byte)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_1.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also see the actual trained weights for each node in each layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'name': 'dense_3', 'trainable': True, 'dtype': 'float32', 'batch_input_shape': (None, 1), 'units': 1, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}\n",
      "[array([[-0.6895044]], dtype=float32), array([0.32920608], dtype=float32)]\n",
      "\n",
      "\n",
      "{'name': 'dense_4', 'trainable': True, 'dtype': 'float32', 'units': 10, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}\n",
      "[array([[-0.72253096,  0.79093796,  0.27218696,  0.9105667 , -0.6352566 ,\n",
      "        -0.40457767,  0.22399843,  0.49433786, -0.03414917, -0.24341366]],\n",
      "      dtype=float32), array([ 0.0000000e+00,  1.1913114e-04, -2.1499033e-01,  2.2037826e-04,\n",
      "        0.0000000e+00,  0.0000000e+00, -1.9898903e-01,  1.2259156e-04,\n",
      "        0.0000000e+00,  0.0000000e+00], dtype=float32)]\n",
      "\n",
      "\n",
      "{'name': 'dense_5', 'trainable': True, 'dtype': 'float32', 'units': 1, 'activation': 'tanh', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}\n",
      "[array([[ 0.22959828],\n",
      "       [ 0.48189473],\n",
      "       [-1.0964584 ],\n",
      "       [ 0.73299795],\n",
      "       [-0.17269206],\n",
      "       [-0.28832698],\n",
      "       [-1.0419734 ],\n",
      "       [ 0.50270987],\n",
      "       [ 0.29819256],\n",
      "       [ 0.3125605 ]], dtype=float32), array([-0.63716054], dtype=float32)]\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Weights of Model_1\n",
    "\n",
    "for layer in model_1.layers:\n",
    "    g = layer.get_config()\n",
    "    h = layer.get_weights()\n",
    "    print(g)\n",
    "    print(h)\n",
    "    print('\\n')"
   ]
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    "We can observe the weights and bias terms associate with our neural network.\n",
    "\n",
    "#### Comments\n",
    "\n",
    "This was a basic demonstration of artificial neural networks. \n",
    "\n",
    "The Texas Center for Geostatistics has many other demonstrations on the basics of working with DataFrames, ndarrays, univariate statistics, plotting data, declustering, data transformations, trend modeling and many other workflows available [here](https://github.com/GeostatsGuy/PythonNumericalDemos), along with a package for geostatistics in Python called [GeostatsPy](https://github.com/GeostatsGuy/GeostatsPy). \n",
    "  \n",
    "We hope this was helpful,\n",
    "\n",
    "*Michael* and *Honggeun*\n",
    "\n",
    "***\n",
    "\n",
    "#### More on Michael Pyrcz and the Texas Center for Data Analytics and Geostatistics:\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "*Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions*\n",
    "\n",
    "With over 17 years of experience in subsurface consulting, research and development, Michael has returned to academia driven by his passion for teaching and enthusiasm for enhancing engineers' and geoscientists' impact in subsurface resource development. \n",
    "\n",
    "For more about Michael check out these links:\n",
    "\n",
    "#### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "#### Want to Work Together?\n",
    "\n",
    "I hope this content is helpful to those that want to learn more about subsurface modeling, data analytics and machine learning. Students and working professionals are welcome to participate.\n",
    "\n",
    "* Want to invite me to visit your company for training, mentoring, project review, workflow design and / or consulting? I'd be happy to drop by and work with you! \n",
    "\n",
    "* Interested in partnering, supporting my graduate student research or my Subsurface Data Analytics and Machine Learning consortium (co-PIs including Profs. Foster, Torres-Verdin and van Oort)? My research combines data analytics, stochastic modeling and machine learning theory with practice to develop novel methods and workflows to add value. We are solving challenging subsurface problems!\n",
    "\n",
    "* I can be reached at mpyrcz@austin.utexas.edu.\n",
    "\n",
    "I'm always happy to discuss,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "Michael Pyrcz, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n",
    "\n",
    "#### More Resources Available at: [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n"
   ]
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